On The Comparison: Random Forest, SMOTE-Bagging, and Bernoulli Mixture to Classify Bidikmisi Dataset in East Java

Iriawan, Nur; Fithriasari, Kartika; Ulama, Brodjol Sutijo Suprih; Suryaningtyas, Wahyuni; Pangastuti, Sinta Septi; Cahyani, Nita; Qadrini, Laila

doi:10.1109/cenim.2018.8711035

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…This latent variable, therefore, would consist of defined vector latent membership. The complete likelihood of BMM would be as shown in Equation (2).…”

Section: Bernoulli Mixture Modelmentioning

confidence: 99%

“…Explanations of the pre-processing identification technique used to construct the Bernoulli mixture distribution at the micro-level, the response variable (Y), and the predictor variable at the micro-level (X) were provided by Iriawan [2]. However, we changed the data scale for the predictor variable X 12 (fourth-semester ranking) and X 13 (fifth-semester ranking) into a ratio scale.…”

Section: Applicationmentioning

confidence: 99%

“…The Bernoulli distribution is frequently used for data mining, particularly for text analysis, and it has been improved into a mixture model called the Bernoulli Mixture Model (BMM) [1]. The BMM evolves on the basis of the mixture distribution, representing data patterns from a data-driven analysis perspective [2]. The expansion of BMM was discussed by Grim et al [3], González et al [4], Juan and Vidal [5,6], Patrikainen and Manilla [7], Bouguila [8], Zhu et al [9], Sun et al [10], Tikka et al [11], Myllykangas et al [12], and Saeed et al [13].…”

Section: Introductionmentioning

confidence: 99%

“…These methods include the BMM, random forest, and SMOTE-Bagging. Based on the Area Under Curve (AUC) and geometric mean (g-mean) values, the BMM using the Gibbs Sampler algorithm run in Software Open-Bugs performs better than the random forest and SMOTE-Bagging [2]. For further research, a comparison between Hibermimo with random forest and SMOTE-Bagging which accommodates the mixture architecture with equivalent computational methods and analyzed using appropriate assays is highly recommended.…”

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confidence: 99%

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On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

et al. 2021

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The model developed considers the uniqueness of a data-driven binary response (indicated by 0 and 1) identified as having a Bernoulli distribution with finite mixture components. In social science applications, Bernoulli’s constructs a hierarchical structure data. This study introduces the Hierarchical Bernoulli mixture model (Hibermimo), a new analytical model that combines the Bernoulli mixture with hierarchical structure data. The proposed approach uses a Hamiltonian Monte Carlo algorithm with a No-U-Turn Sampler (HMC/NUTS). The study has performed a compatible syntax program computation utilizing the HMC/NUTS to analyze the Bayesian Bernoulli mixture aggregate regression model (BBMARM) and Hibermimo. In the model estimation, Hibermimo yielded a result of ~90% compliance with the modeling of each district and a small Widely Applicable Information Criteria (WAIC) value.

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“…This latent variable, therefore, would consist of defined vector latent membership. The complete likelihood of BMM would be as shown in Equation (2).…”

Section: Bernoulli Mixture Modelmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

et al. 2021

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SMOTE Classification and Random Oversampling Naive Bayes in Imbalanced Data : (Case Study of Early Detection of Cervical Cancer in Indonesia)

Rahmi

Wardhani

Mitakda

et al. 2022

2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA)

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Teknik Random Forest untuk Meningkatan Akurasi Data Tidak Seimbang

Nugroho,

Harini

2024

jsitik

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Data tidak seimbang terjadi karena jumlah data pada tiap kelas berbeda jauh dimana akan mempengaruhi hasil prediksi. Dalam penelitian ini menggunakan dataset prediksi diabetes, yang mengandung data yang tidak seimbang. Hasil prediksi ditunjukkan dengan nilai akurasi dan presisi. Tujuan penelitian ini adalah meningkatkan nilai akurasi dan presisi pada data yang tidak seimbang. Metode yang digunakan dalam penelitian ini adalah penentuan sampling dan pembelajaran ensemble. Penentuan sampling yang digunakan adalah dengan cara mengalikan data pada kelas minoritas atau oversampling. Teknik Oversampling yang digunakan adalah Synthetic Minority Oversampling Technique (SMOTE). Pembelajaran ensemble yang digunakan adalah algoritma random forest. Kombinasi algoritma SMOTE dan random forest dapat meningkatkan akurasi dan menyeimbangkan nilai presisi pada setiap kelas. Hasil penelitian ini adalah Kombinasi tersebut menghasilkan nilai akurasi sebesar 97,5% dan nilai presisi pada kelas non pasien sebesar 97% sedangkan nilai presisi pada kelas pasien sebesar 98%.

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On The Comparison: Random Forest, SMOTE-Bagging, and Bernoulli Mixture to Classify Bidikmisi Dataset in East Java

Cited by 3 publications

References 12 publications

On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

SMOTE Classification and Random Oversampling Naive Bayes in Imbalanced Data : (Case Study of Early Detection of Cervical Cancer in Indonesia)

Teknik Random Forest untuk Meningkatan Akurasi Data Tidak Seimbang

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